Climate change is projected to reduce raw water quality, posing risks to drinking water even with conventional treatment. This work closely aligns with the United Nation’s 2030 sustainable development goals pertaining to clean water and life below water. We selected six California reservoirs to measure cyanobacteria, which may be harmful if ingested, using CIcyanodata provided from the Cyanobacteria Assessment Network. Primary land class, post-fire soil loss erosion, and wildfire frequency were considered to determine if they influence cyanobacterial trends. We found a slight increasing CIcyanotrend for all water pixels in the lake and a greater increase when only considering CIcyanopixels and the percentage of the lake covered with CIcyanopixels. Higher soil loss and wildfire frequency did not lead to higher a CIcyano, but land class may have a greater influence. Future work incorporating temperature and drought may help better understand cyanobacteria dynamics.

This study investigates the effects of post-wildfire sediment runoff on giant kelp (Macrocystis pyrifera) populations in Malibu, California, following the 2018 Woolsey Fire. The research utilizes satellite data and the Soil and Water Assessment Tool (SWAT) to assess changes in sediment delivery and its correlation with reduced kelp abundance. Findings indicate a significant decrease in kelp canopy recovery, post-fire, with ongoing limited regrowth up to 2023. This study underscores the critical impacts of wildfire-induced sediment runoff on marine ecosystems and emphasizes the need for tailored coastal management and restoration strategies to mitigate these effects and support the resilience of kelp forests, which are vital for biodiversity and coastal ecosystem services.

BioSCape, or the Biodiversity Survey of the Cape, is NASA’s first biodiversity-focused integrated remote sensing field campaign. The campaign aims to better understand the structure, function, and composition of the region’s ecosystems, and to learn about how and why they are changing in time. To do this, airborne and field data were collected across aquatic and terrestrial ecosystems in the Greater Cape Floristic Region in southwestern South Africa in 2023. Airborne acquisitions included data from three imaging spectrometers sampling across the electromagnetic spectrum (UVSWIR and TIR) and coincident full-waveform lidar data. Field datasets included measurements to quantify the diversity of plant communities including alien invasives and kelp, phytoplankton functional types, phylogenetic histories, eDNA in watersheds, bird and frog acoustics, plant functional and spectral traits, blue carbon, and in-water radiometry. BioSCape’s airborne and field datasets are diverse and complex, and the airborne data in particular is not inherently easy to use. In this paper we outline the procedures BioSCape followed to ensure maximum impact of the data and support of Open Science and FAIR data principles [1].

This study aims to detect different irrigation treatments in sorghum plant breeding experiments using multispectral satellite imagery coupled with a random forest classification model. Obtained from Planet Labs' official repository, satellite images span three sorghum test plots in Central Valley, CA, USA, covering a range of soil and climatological conditions. At each test plot, three irrigation treatments were applied: 100% full irrigation, 70% reduction, and 50% reduction to assess the drought resistance of different sorghum cultivars. A Random Forest Classification algorithm was used to detect the different treatments with high accuracy across all sites. The findings illustrate (1) the predictive capacity of PlanetScope Multispectral imagery for differences in irrigation across different sites and cultivars of sorghum, (2) the efficacy of Random Forest methods in accurately distinguishing diverse irrigation treatments using multispectral satellite imagery, and (3) the demonstrable advantage of utilizing multitemporal data over single-day imagery-based classification approaches. This work is an important step in advancing the utility of satellite remote sensing in irrigation monitoring water resources management, plant breeding studies, and supporting precision agriculture applications.

Interferometric Synthetic Aperture Radar (InSAR) time series analysis is a powerful technique to estimate long-term water level changes in wetlands ecosystems. However, few studies have applied InSAR on wetlands that are highly segmented by canals and levees due in part to the challenge of selecting qualified reference points to minimize unwrapping errors, which, by contrast, is a relatively easy task for unsegmented wetlands. Here we developed a new method to automatically select the optimal reference point for InSAR time series analysis. The method selects reference points by considering temporal behaviors of coherence and InSAR phase connectivity from each reference point to its wetland of interest. We tested the method on six managed and highly segmented wetland units within the Sacramento National Wildlife Refuge in the Central Valley, California. We validated the InSAR measurement against water depth gauge measurements during a low water depth (<10 cm) period in 2017. The overall accuracy of the estimated water depth changes achieved an RMSE of 1.49 cm. Compared with three existing methods, our method showed significantly lower RMSE values overall. This new automatic method enables us to maximize the performance of InSAR to predict water depth and could be applied to other types of InSAR applications as well.

Wildfires in coastal-draining watersheds can lead to increased sediment and carbon export, affecting macroalgae abundance and distribution. This study aims to characterize the impact of changing light conditions due to wildfire runoff on giant kelp (Macrocystis pyrifera) along coastal California, USA. Focusing on four historically dense giant kelp forests that receive drainage from a recently burned watershed-Malibu, Ventura, Santa Barbara, and Big Sur-we evaluate pre- and post-fire terrestrial changes, and fraction of giant kelp canopy biomass. The Soil and Water Assessment Tool (SWAT), a watershed model, is used to simulate river discharge and sediment loads, while satellite-derived kelp canopy biomass and turbidity products are compared to model results. Thus far, model outputs and time series statistics have indicated a relative increase in sediment load and discharge after the Woolsey wildfire near Malibu, California and a relative decrease in kelp biomass after a wildfire event in the four watersheds.

We present the preliminary results of a study conducted by the Commonwealth Scientific and Industrial Research Organisation (CSIRO, Australia’s national science agency) and NASA’s Jet Propulsion Laboratory (JPL) to demonstrate the utility of imaging spectroscopy from space to provide actionable information on water quality and aquatic ecosystems for Australia and Western USA. Mission requirements are derived from three key application objectives: potentially harmful algal blooms and nutrient pollution, invasive aquatic vegetation, and coral reef habitat benthic cover. The proposed AquaSat-1 instrument is a state-of-the-art visible to near-infrared (VNIR) Dyson imaging spectrometer, which builds on over 30 years of imaging spectroscopy development at JPL.

Biodiversity loss jeopardizes human wellbeing and sustainable development and is the target of multiple global, national, and regional conservation goals advocating for increasing the integrity of all ecosystems. Quantifying ecosystem integrity can be done by examining the state and trends of biodiversity in a system. Recent advances in remote sensing capabilities offer exciting opportunities to do this, however, for these data to answer the most important ecological and evolutionary questions, they need to be integrated with traditional field-based measurements [1] .

High resolution fractional vegetation cover (HR-FVC) is important for many applications, including precision agriculture, forestry, and conservation. For land managers, HR-FVC is most useful when the data can be produced quickly with minimal effort. In this study, we perform data fusion of RGB drone data and multispectral cubesat data for synthetic daily HR-FVC estimation. First, binary classification of 10cm resolution drone data was used to identify vegetation. An AdaBoost model (Accuracy = 0.868, F1-score = 0.840) was selected for further analysis. HR-FVC training data was then produced from drone vegetation maps by calculating the FVC in a 3m pixel – Planet SuperDove resolution, resulting in 238,270 training points. A random forest regression model was used to predict HR-FVC from Planet SuperDove data. The final model’s performance is comparable to similar studies (R2= 0.720, RMSE = 0.213), suggesting the methodology could be viable for applications requiring daily HR-FVC datasets.

California has lost most of its historical wetlands and it is in urgent need to conserve and protect the remaining wetlands. One of the key elements for wetland management is monitoring changes in surface water depths, which is challenging due to inaccessibility and dynamic hydrology of wetlands. Particularly, many California wetlands are privately owned with small areas (e.g., 40 ha) and bounded by levees, resulting in differences in hydrological regimes. Managed wetlands are characterized by high water depths in winter season and low depths in summer season, with a rapid transition in between. Considering the complicated spatiotemporal hydrological pattern, it is difficult to monitor regional water level variations based on in-situ measurement alone. This study explored the possibility of using multi-sensor observations for hydrological applications by investigating the relationship between satellite observations (Interferometric Synthetic Aperture Radar (InSAR) coherence, and Normalized Difference Vegetation Index (NDVI)) and in-situ water depth measurements.

Remote sensing is a powerful tool for tracking surface waterbodies at different scales and spatial and temporal resolutions. The efficient management of water resources, at basin or regional scales, requires the monitoring of water storage in reservoirs. In reservoirs where storage observations$\text{are}$available or a surface water-storage curve exists, storage can be estimated using optical remote sensing. Therefore, this study uses image segmentation to estimate the surface area of three large reservoirs to predict surface water storage volumes. We established a surface area-storage relationship using the Modified Normalized Difference Water Index (MNDWI), produced from Landsat imagery using the Google Earth engine code editor interface, to estimate surface water storage for the New Melones, Don Pedro and McClure reservoirs located in central California. Observed storage showed a very high correlation$(\mathrm{R}\geq 0.990)$with remotely sensed surface area estimates. The results show that the modeled storage values derived from fitted equations of the remote sensing methodology were highly correlated$(\mathrm{R}\geq 0.993$, p-value$< 0.001)$with observed storage data. Monitoring surface water storage using satellite data is thus demonstrated, as is seasonal and interannual variations of storage levels, which are estimated with small errors and high predictive power$(\mathrm{R}^{2}\geq 0.987,\text{PBIAS}\leq+0.6\%$,$\text{NSE} \geq 0.989)$.

The San Francisco Estuary and Sacramento–San Joaquin River Delta (Bay Delta) is a highly sensitive and critical habitat for the Delta Smelt, an endangered endemic fish, with water temperature being a key determinant of habitat suitability. This study investigates the relationship between open water surface and subsurface conditions from spaceborne thermal measurements (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and Landsat-8) andin situsensor data from the California Data Exchange Center (CDEC) to produce estimates of spatially continuous bulk temperature in the Bay Delta. We found that ECOSTRESS and Landsat-8 surface temperature measurements are well-correlated with bulk water temperatures ($N =236$and$r = 0.907$and$N = 226$and$r = 0.976$, respectively). For the ECOSTRESS-in situcomparison, accounting for time of day improved the correlation between surface and subsurface conditions ($r = 0.946$, 0.881, and 0.944 for morning, midday, and evening, respectively). We found that ECOSTRESS surface temperatures were warmer than bulk temperatures in the midday period (2 °C peak at 2 P.M.) and cooler in the morning and evening periods (−1°C peak at 6 A.M.). We also found that a simple harmonic regression model can capture the diurnal variability of the skin effect to predict bulk water temperature (root-mean-square error (RMSE) = 0.809°C). With ECOSTRESS, we found that across the Bay Delta, including open waters and pelagic bays, temperature conditions causing stress and mortality for the Delta Smelt were persistent throughout the day during summer months. ECOSTRESS is a unique dataset capable of informing conservation efforts in the Bay Delta.

Wildfires leave significant impacts upon many ecosystems. In California, the Mendocino Complex Fire was the second largest fire in California history. This fire left a sizable burn scar within the Mendocino National Forest. This work examines normalized burn ratio to understand the burn severity to formulate insight on wildfire impacts to land albedo and its recovery.

Optical remote sensing in complex waters is challenging because the optically active constituents may vary independently and have a combined and interacting influence on the remote sensing signal. Additionally, the remote sensing signal is influenced by noise and spectral contamination by confounding factors, resulting in ill-posedness and ill-conditionedness in the inversion of the model. There is a need for inversion methods that are less sensitive to these changing or shifting spectral features. We propose WaveIN, a wavelet-enhanced inversion method, specifically designed for complex waters. It integrates wavelet-transformed high-spectral resolution reflectance spectra in a multiscale analysis tool. Wavelets are less sensitive to a bias in the spectra and can avoid the changing or shifting spectral features by selecting specific wavelet scales. This paper applied WaveIN to simulated reflectance spectra for the Scheldt River. We tested different scenarios, where we added specific noise or confounding factors, specifically uncorrelated noise, contamination due to spectral mixing, a different sun zenith angle, and specific inherent optical property (SIOP) variation. WaveIN improved the constituent estimation in case of the reference scenario, contamination due to spectral mixing, and a different sun zenith angle. WaveIN could reduce, but not overcome, the influence of variation in SIOPs. Furthermore, it is sensitive to wavelet edge effects. In addition, it still requires in situ data for the wavelet scale selection. Future research should therefore improve the wavelet scale selection.

This letter presents an application of continuous wavelet analysis, providing a new semi-empirical approach to estimate Chlorophyll-a (Chl-a) in optically complex inland waters. Traditionally spectral narrow band ratios have been used to quantify key diagnostic features in the remote sensing signal to estimate concentrations of optically active water quality constituents. However, they cannot cope easily with shifts in reflectance features caused by multiple interactions between variable absorption and backscattering effects that typically occur in optically complex waters. We use continuous wavelet analysis to detect Chl-a features at various wavelengths and frequency scales. Using the wavelet decomposition, we build a 2-D correlation scalogram between in situ pond reflectance spectra and in situ Chl-a concentration. By isolating the most informative wavelet regions via thresholding, we could relate all five regions to known inherent optical properties. We select the optimal feature per region and compare them to three well-known narrow band ratio models. For this experimental application, the wavelet features outperform the NIR-red models, while fluorescence line height (FLH) yield comparable results. Because wavelets analyze the signal at different scales and synthesize information across bands, we hypothesize that the wavelet features are less sensitive to confounding factors, such as instrument noise, colored dissolved organic matter, and suspended matter.

Consistent and accurate information on inland water quality over wider areas of the Australian continent are required to assess current condition and trends in response to key environmental and climatic impacts. Optical remote sensing offers a method to objectively assess this over multiple spatial scales provided retrieval algorithms are accurate. Here, we present the results of initial research aimed at exploring the optical variability in Australian inland waters and of linear matrix inversion algorithms applied to both in situ reflectance spectra and high resolution satellite data to retrieve water inland water quality parameters. In situ sampling reveals a high degree of optical variability both within and between lakes across the regions sampled with regional patterns evident; sub-tropical and tropical lakes exhibited greater optical complexity than deep lakes in mid-latitude regions. Clustering analysis indicated the presence of 8 different optical water types in the water bodies measured. The ability of the linear matrix inversion algorithm to map water quality, tested on in situ reflectance and WorldView2 image datasets, showed relative accuracy when parameter sets were sufficient to achieve algorithm closure. Improved algorithm parameterization will be required to account for the high degree in spatial and temporal optical variability observed in Australian inland waters.

This paper argues for the development of a quantitative global inland water quality product based on satellite optical remote sensing. Water quality is a critical component of global fresh water security and ecosystem health, yet is often overlooked when global analyses of water security are undertaken. In the face of declining surface measurements and datasets across the globe, alternatives to conventional water quality measurement are required. The case for an optical remote sensing based inland water quality product is a strong one. Global products of ocean color and their dissemination infrastructure are operational and widely used, providing a framework for global inland products. Furthermore, coastal and inland water quality algorithms based on spectral inversion algorithms are now sufficiently mature to cope with the greater variability of inherent optical properties in these systems. While these algorithms provide the greatest promise for reliable, robust and simultaneous retrieval of several water quality variables across sensors, limited knowledge of the bio-optical properties of inland waters and limited validation currently prevent global implementation. Internationally coordinated efforts are required to accumulate representative bio-optical data to improve our understanding of the optical complexity and variability of inland waters.